r - Use of apply instead of loops for a matrix -


i have matrix (all_parameters) 150 columns , 100.000 rows. value each data element in matrix "1". replace values "0" if following conditions true:

col name either 10, 14, 27 row name starts "t1_"

i have following loop works fine: 

t1_missing = c(10,14,27)  for(i in 1:ncol(all_parameters)) {   if (any(t1_missing == as.integer(colnames(all_parameters)[i]))) {      for(j in 1:nrow(all_parameters)) {       if(grepl("^t1_", rownames(all_parameters)[j])) {         all_parameters[j,i] <- 0       }     }   } }

the problem execution of loops takes extraordinary long time. tried use apply function not able make work. can please show me how solved using apply function (or else superior , faster on for-loop).

thanks help!

would simple vectorized operation:

all_parameters[ grepl("^t1_", rownames(all_parameters) ), t1_missing] <- 0

further conditions on rows added either & (which might make conditions more restrictive) or | (to include more rows). assumed use of term "col names" meant refer these numeric position. using [ i, j]-operations, can mix logical indices in i-rows numeric indices in j-columns. (now tested example jbaums: since deleted reproduced here:

m <- matrix(1, ncol=30, nrow=12,          dimnames=list(paste0('t', rep(1:3, each=4), '_', rep(1:4, 3)),                       1:30))

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